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@Spacerat Spacerat/ml.ipynb
Created Feb 28, 2015

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Machine Learning Examples
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cross_validation import train_test_split\n",
"import pylab as pl\n",
"from matplotlib.colors import ListedColormap\n",
"from sklearn.preprocessing import scale\n",
"import cvxopt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data and Plotting"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 40\n",
"# Two 2-d Normal datasets\n",
"'''\n",
"X1 = vstack((random.normal(4, scale=6, size=N/2),random.normal(0, scale=5, size=N/2))).T\n",
"X2 = vstack((random.normal(4, scale=6, size=N/2),random.normal(0, scale=2, size=N/2))).T\n",
"\n",
"X3 = vstack((random.normal(0, scale=4, size=N),random.normal(0, scale=5, size=N))).T\n",
"\n",
"y1 = -ones(len(X1)+len(X2), dtype=int)\n",
"y2 = ones(len(X3), dtype=int)\n",
"'''\n",
"\n",
"mean1 = [-1, 2]\n",
"mean2 = [1, -1]\n",
"mean3 = [4, -4]\n",
"mean4 = [-4, 4]\n",
"cov = [[1.0,0.8], [0.8, 1.0]]\n",
"X1 = np.random.multivariate_normal(mean1, cov, 50)\n",
"X1 = np.vstack((X1, np.random.multivariate_normal(mean3, cov, 50)))\n",
"y1 = np.ones(len(X1))\n",
"X2 = np.random.multivariate_normal(mean2, cov, 50)\n",
"X2 = np.vstack((X2, np.random.multivariate_normal(mean4, cov, 50)))\n",
"y2 = np.ones(len(X2)) * -1\n",
"\n",
"# Concatenate for full dataset\n",
"X = concatenate((X1, X2))\n",
"y = concatenate((y1, y2))\n",
"\n",
"#X = scale(X)\n",
"\n",
"len(y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"200"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Tester():\n",
" '''Class for plotting example classification results'''\n",
" \n",
" def __init__(self, X, y):\n",
" # Split data in to test/train segments\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
" print len(X_train), len(X_test)\n",
" self.X_train = X_train\n",
" self.X_test = X_test\n",
" self.y_train = y_train\n",
" self.y_test = y_test\n",
" self.X = X\n",
" self.y = y\n",
" \n",
" def get(self):\n",
" return (self.X_test, self.X_train, self.y_test, self.y_train)\n",
" \n",
" def predict(self, predictor):\n",
" return predictor.fit(self.X_train, self.y_train).predict(self.X_test)\n",
" \n",
" def __call__(self, predictor):\n",
" h=0.05\n",
" # Predict class of all values on a grid\n",
" x_min, x_max = self.X[:, 0].min() - 3.5, self.X[:, 0].max() + 3.5\n",
" y_min, y_max = self.X[:, 1].min() - 3.5, self.X[:, 1].max() + 3.5\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
" Z = np.c_[xx.ravel(), yy.ravel()]\n",
" \n",
" # Call the fit()/predict() methods\n",
" Z = predictor.fit(self.X_train, self.y_train).predict(Z)\n",
" \n",
" # Contour plot the prediction\n",
" Z = Z.reshape(xx.shape)\n",
" cm = pl.cm.RdBu\n",
" cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
" contourf(xx, yy, Z, cmap=cm)\n",
" \n",
" # Scatter plot the test/train values\n",
" scatter(self.X_train[:, 0], self.X_train[:, 1], c=self.y_train, cmap=cm_bright)\n",
" scatter(self.X_test[:, 0], self.X_test[:, 1], marker='x', c=self.y_test, cmap=cm_bright,alpha=0.6)\n",
"\n",
" score = sum(predictor.predict(self.X_test) == self.y_test)\n",
" print \"Accuracy: \", float(score)/len(self.X_test)\n",
" \n",
"test = Tester(X, y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"150 50\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Kernels"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class NoTransform():\n",
" def fit(self, X, y=None):\n",
" return self\n",
" def transform(self, X):\n",
" return X\n",
" \n",
"class BiasTransform():\n",
" def fit(self, X, y=None):\n",
" self.R = sqrt(max((sum((X - mean(X, axis=0))**2, axis=1))))\n",
" return self\n",
" \n",
" def transform(self, X):\n",
" # Add a dummy input variable to every observation\n",
" return np.column_stack((X, self.R*ones(X.shape[0])))\n",
"\n",
"class RadialKMeansTransform():\n",
" def __init__(self, k=None):\n",
" self.def_k = k\n",
" def fit(self, X, y=None):\n",
" if self.def_k:\n",
" k = self.def_k\n",
" else:\n",
" k = len(set(list(y)))\n",
" self.clusters = KMeans(k).fit(X).means\n",
"\n",
" return self\n",
" def transform(self, X):\n",
" return exp(-vstack([sum((X-m)**2, axis=1) for m in self.clusters]).T)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"scatter(X[:,0], X[:,1], c=['r' if yy < 0 else 'c' for yy in y], s=50)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.collections.PathCollection at 0x111fc9810>"
]
},
{
"metadata": {},
"output_type": "display_data",
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pU7755htCQ0Np0KAB3bp1S2KKerd+fZb26AF37oCXF5w5A3/+CUIQ6+yMk5MT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\nKSUTuCRJUin1fyhaQHMtLcw3AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111f69810>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KNN"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class KNN():\n",
" def __init__(self, k=5):\n",
" # Set the K of KNN\n",
" self.k = k\n",
" \n",
" def fit(self, X, y):\n",
" # Just store the training data\n",
" self.X_train = X\n",
" self.y_train = y\n",
" return self\n",
" \n",
" def predict(self, X):\n",
" def pred(x):\n",
" # Get distance of the point to each training point\n",
" dists = sqrt(sum((self.X_train - x)**2, axis=1))\n",
" \n",
" # Get a list of the classes of the K closest points\n",
" ns = list(self.y_train[argsort(dists)[:self.k]])\n",
" \n",
" # Return the most common point\n",
" return max(set(ns), key=ns.count)\n",
" \n",
" return apply_along_axis(pred, 1, X)\n",
" \n",
"figure()\n",
"test(KNN(3))\n",
"figure()\n",
"test(KNN(10))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 1.0\n",
"Accuracy: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1.0\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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wfTXStnp8P5lskRdZMUbEm503XUqGx5vpZ6UgxrSHzNrwshLzJKBquSiOoDeS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\nuYmmFVcjRCXS3o5/5IUY+WU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j9/tNc3OzyczMNIFAwMZKnaOqqsps2bLF7jIc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\nB6eC5/Fgh5CLAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11289bdd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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0aB6GWRHGmI1IBNmOyS5PVo/NJ10lIhQuStAVR9EdGXmaP4p/2GZi26cCEs+A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\nRdDHxbzX1l7JEFqjnnqwJoudCCMTjbj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"text": [
"<matplotlib.figure.Figure at 0x112879850>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1668
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Perceptron"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Perceptron():\n",
" def __init__(self, kernel='linear', k=None):\n",
" if kernel == 'linear':\n",
" self.kernel = BiasTransform()\n",
" elif kernel == 'kmeans':\n",
" self.kernel = RadialKMeansTransform(k)\n",
" \n",
" def fit(self, X, y):\n",
" # Kernal transformation of X\n",
" perm = permutation(range(len(X)))\n",
" X = X[perm]\n",
" y = y[perm]\n",
" self.kernel = self.kernel.fit(X, y)\n",
" dX = self.kernel.transform(X)\n",
"\n",
" # Vector of weights\n",
" # number of weights = number of basis functions\n",
" w = zeros(dX.shape[1])\n",
" \n",
" # Store best weights/score\n",
" best_w = w\n",
" best_score = len(dX)\n",
"\n",
" # Repeat algorithm 100 times\n",
" for rep in range(500):\n",
" # Loop through observations\n",
" for xk, yk in zip(dX, y):\n",
" # If the observation is misclassified\n",
" if yk * dot(xk, w) <= 0:\n",
" # Add its vector to the weights\n",
" w += yk*xk\n",
" \n",
" # Calculate number of errors\n",
" score = sum(y*dot(dX, w)<0)\n",
" \n",
" # Store weights if they score better than the previous\n",
" # best\n",
" if score < best_score:\n",
" best_score = score\n",
" best_w = w\n",
" \n",
" # Finish if there are no errors\n",
" if score == 0:\n",
" break\n",
" print \"{} iterations, {} errors\".format(rep, score)\n",
"\n",
" self.w = best_w\n",
" print best_score\n",
" return self\n",
" def predict(self, X):\n",
" X = self.kernel.transform(X)\n",
" return array(((dot(X, self.w)) > 0)*2-1)\n",
" \n",
"figure()\n",
"perp = Perceptron(kernel='linear', k=4)\n",
"test(perp)\n",
"perp.w\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"499 iterations, 47 errors\n",
"47\n",
"Accuracy: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.66\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"array([ 4.75346221, 2.70232495, 0. ])"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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hTF8aD9SWUeRMBW7Hnft6GjrLEfiw9rrJGeAUrg9uZbgnhSqnKx8k7gTgKv/9\nDe5sah9+yH0PvtHEVwrSNjHLNyEdB0+XAjRP88zcPaF8E98vXMymlK481e901ctu51QPXWk3Cu0E\nRXYAi9dw770PQWMwffVJnOPrylEeB4AyJw5cWLeXH5iAjUSwFNiKm/+OYrOJVOHFlF4WmhfQQ1tH\njezKN/aJDDa+bFCbnHPOgTNubdLrdMwY5pxH6RWvxg+s1b34Tr8LPZjRpOcJWElOLN9MjSdAt3gt\nufFqigNNew6l/VOjXJRmYSERhNjzI+bHIMo1/mfrgzlADz2IOzQR3LHmb6MRZJgRxMtx6JyNRi9y\ntDDdNA9fW6eRIXZxuvclzvL+i/XWCVQ7XRvcrimznm6iK3SZaz/me5FyVloJFlkJ7kqEkSveBEBK\nSbxwMdGv3yO+7SvM6mLihYtJlm06eC/LselXW1L/OCMZ5ZaNn7E12IWpQ8/nzZ4juWnT5/hs84DH\nUDon1UNXmkUv3UdA7CQpf4vDxeg8SxfNpuu3buLdGEzlf2v/TJTHcVMuQ/GxiKsDucxOVPFJcjkO\nd1HsLOd/w59wT8pMgkIiBIRENef5nkQX1mG1bceTE+lxx7tNcp1GuJRzpV1fsX0ckid3rSW+8r/I\ncCm9yzZypZ3kdU1ns+MwRjNYJgTRnsfgGXnFfo+ZmYxyzdYF/DfvGLaGujBpwxwWZfXhk5whIAQr\nM3qyNdSFRDu6qau0DJVDV5pNpWPycizMTtumt25wVSCVlP3cyLOlZHaikrUWZOqCc30pZGkefl5d\nRIy5uNP1wcs5XBZYzmBjGFmiCE1IpIRSpzfd9G2H1bZb1rzbJEE9vm4mQ1d/wEzcyjJXADtwl+B4\nGDgTd9VTDXdK0ydAL6C/7iVxyiQ8WX32e9zcWBV3f/MxAO/2OJrPug1sdFuVtqU5cujNlnKZMWMG\ngwcPZsCAATz00EPNdRqlDcvUPPwolMnUtGxuCWXsN5iDW5vmLH8WP0rJ4upAJlma2/M0MXHDn8sm\nn7jjsMo8nfnmpThSsMwaz2JzArZswIiPvTwzZCJDTj32iK9tN0+fE1mJWx4sE1iHO/1pCvAE8DXu\nekiBum1qcRfSGyo0nHj1AY8b073131d7Avu8JqXESUY6fB0a5fA1S0C3bZs777yTGTNmsHr1aqZN\nm8aaNWua41RKBzbcSMfgBmA98C46L3OUJ8QY76skZIBX4pMptvtzpu8FdHH4we3KqZMa3UbdF0IP\nZnIfMAG+EtA+AAAgAElEQVS4CwjVvZYL9ASmA/8LbMC97bsIWChtjPS8/R4zPRll0oY5vNvjaP46\naBwXFi1jRFUh4NZ1j384leiMKdS8dy+JwiWNvgal42iWgL5w4UL69+9Pfn4+Ho+HK6+8kv/+97/N\ncSqlA7sumM6xnoWkiFF0025gUiiDXN2HhkV63dhzv6jFQ/yIz7HlWufQGx2CcfJtPBDI5L8I7gXm\n4K58+kvcIG8DH+EuWX0qMAaBGHYheqjLfo+X1Aw+6j6Uz7oNpDiQwTP9xhA2/Egpsb54mj/Fq4k6\nNoscC7nsNXfxDkWhmW6KFhUV0avXno/KPXv2ZMGCBftsM3ny5Prvx44dy9ixY5ujKUo75hUa139r\n+J+UsMwaT4nTm4v8f2FB8kLmm5dykucNNHH492Wev+lZpgD3N2I4o5HSFX38b/GaMaLbvuLCVe8S\nQHIK8AJwNzAEuB94CNgCPL76feycIfsd3hgzvCzZK7e+e3iiNGMk4jXcXvf8cGCM0JhbVYSRmnPE\n7VfartmzZzN79uwGb98sAb0hNbr3DuiKcjhSRCVn+ubiEzFO877Cervxy8jN9H/CWfGzj3h/IQTo\nHvStCxgjNGxpMxdBBMn9wCjcm6MnAyCpNeM4K97io+OuIuzxkxOr5rTS9bzRcyRO3b2GgJUkZuzJ\npQvDh9B0ltoOI4EIsExKNLUWaof17c7ulClTDrp9s6Rc8vLyKCwsrH9cWFhIz55qZRil8YSAAcZX\n+EQMAF1YDDbmH1HvfG/zpm9i6j2XNuoYyZJ1FESreEfafACsQpIABgG7gLS9ts1AUqbp3L5xDv1r\nd3Hbxs/omqjh2q0L0aRD13gtP//mY3Jie26cCqHhO/YqTtM9jDd8DNS9RHuMwNOlb6ParXQczdJD\nP/7441m/fj1btmyhR48evPrqq0ybNq05TqUoTWb3TFLdsRlUu4vV6T0A9yZluhlj2wFy3rtJ26TL\nXmPSM3B7TE8Cv8JdpuMJoBB4VPfgHXQ2vSu3cdvGz3i3x9F8nt2P67bM52fffEIoGeW/ZowdK9/G\nzMrH12+MG9DzjsZOz+PL6u3o/nSMrPx2uWqV0jyapYduGAaPP/4455xzDkOHDuWKK65gyJCOXaZU\n6RimzHqaFCvBxduXcmLZRtKTUW7fMIfe0QoAuseqQUqkY5H8+n1yPv0z1oJ/Y0cr8GT1Yb5j8RSw\nHLgJd4hiD9ylqb9BcFFKN36c2RvjpFvI84boGy6lxJfK6IrNBGyTD3KH0z1WRWj7YtK3L+VPO1Yw\nZM10zEUv1rdRT8nGn3cMni4FKpgr+2i2maITJkxgwoQJzXV4pZ2LS4dyJ0maMEhtYxUA30z7gsv7\nn86v18xAsIT3eoxgXtcBICUX7FhOhTfEtOJV3Fb8NbdImxdrdvKX8k14Tr0dXTN427F4AjgJOB4o\nAioAny8F/exfA+BzbG5c+yHv5h3N0szejNu5mkkb5uC3TV5N68EJhYt5TdpowJW2SdfiVRiJMJov\npdXeF6Xta1u/SUqnsMGK8mSkHMjGYgeX+DMY60s75H4tZd70TcxY9A9mjZsBQGL3HxwheD7/JG7a\n8BkP7VjB9bizQycDn9kWi6qLkR4/VyfCXAcsAM4CLN3LfOmgD5mAVbsLPaUrlqbz2IAzCXv8JHet\n4e3qHVhWnNoew/nCsdkpNCbXtccPGAg1kUg5JBXQlRZlS8lTkQrivIE7Mnszb8VHMsjwkav7Wrt5\nAHilTdmwkfzg+YcZ9vw2Jm2Yg0SwILsvCd3D5pQu9MAdX75bEhCajvfkSfzoi//j1kQtAUCkdOPL\nXsfjKVqKb+XbSMBMy8Vzym2Ul22Ale+QFqvgcgkf6R62R8oxjrua9R4/9zkW50mHJ4SOlt4Dzd92\n/ugpbZMK6EqLCksbCw03mAMUoHMcO521bSagW2hsNlJZeNuDlJ9xKzM9fvLWfkg0uy/fC2Zzyc5V\nPJIzlFjJWkZLh5cRLPf48HUbhObxY+cOJ33LfCqRyHAJ2prpnAz8DUkv4AfVRcxY+DzBso3EHIvF\nQDcgYScpKF1PLFyC97Sf8sTy13kyUobM7IMx4mKVL1cOSQV0pUWlCB0NC5gLjAGKsVlKN631e59V\njsWCZDWlTpKBRojjHC8TZvyJz5Pl/Nqx6F+2AU33srrvqawfdDZ/3DiXs4uWMT2jF76h56F5/DiO\nTXzLFwwHluEubT0BySzgPNyx4z92LOaUbeJvjsU9wO7ivz4gT2hsjdeSFcyi4qSbW+V9UNovFdCV\nFqULwS3BLjwdPRedAiy2ca4vhTy9eVb5aagSO8lfw1sAySjgS7MGXehEpM00oC/uzMyFdpIrvCFI\nhEn6U3lv0Di83Y9C6IY7NX/h8wSBX7MnUP8Pbp59BfAxcClgaDo9HJNM4BFgEjAL2CAlP64uYpu0\n+aDHiBZ8B9qm2OYFRFbPRNpxfLlHk3rsBe1qLdiWpha4UFrcUE+IP6Tmcnuoht+ldmVCG8gNfxIv\nJRfJw7glbr8GKqTNSOBtYDZuj3s1cPKW+cQ//QvjV7zNwCWvkJzzN5xEhGTJN+gl39Ab2Ltk1mJg\nd2geBwQQJAtO5gbdy13Av3ALBBfqXv7abSDlwSw+yB3eItfdliV3rSW8ah7SnAnOZhLFWYSXf9Da\nzWrTVA9daRUpmkH/NjRcMSZtwrhJIHB7OifirqF0ct3z6cBAIBEpIwN4GncEysiaEtbOmEyK0Ig5\nFr8CfoI7Ft3ELac7u+64K4BqTSdl4FlUp2Rzz5YvkboHfeDZGMWr0KTN1+k91HqhQHLXBrB/DBzt\nPuE8RGLXOFJbtVVtm+qhKwrQz0jBAf6GO3qlFHgVd7XTObjBHKA7bo/7G9w0yU6gEIfPpUOZY/E2\ncCfwZt1xPvYEMAacxdm6h1GGn5N1D75jr0Dz+PH1GY12+k8JnHQrt9fsYElmbx4fcCbfL1zE0Ood\nLXr9bZHw+UF8vdcza9G8wVZrT3vQdrpIitKKTvNlUuEkedms5t+AxE2FHAeMr/sahztt3w+8hNtv\n/B7wx7rtYE9K5TyPD19aD0LHXY0ezMTqM4r10Qp8KTnfqbDocSw2pHTj47ol5v7Z91Ry4jUtcNVt\nW6DgZOJbnsJJXASyB2jTSDn6qtZuVpumlqBTlG8psuM8Ey7kKCRhJIVCJyoduiIpAx4ELqn7tyvw\nBYJ/IMkD1gDHagahCZPRvrXSUEclLQN0qz5LJC0DYRzeOq8H4pgxEtuXIe0k3pzBHapMcHMsQad6\n6IryLXm6n1+n9WW9FUMHrjGCRKXNI7VbGY9NFLjQa3BrqAdaWg/mBbMYuu5jhgqdldLGN+KSThPM\nAeLbjkLzR/DmbsCuzSJeeBShwfMReuODuuYJECg4qQla2TmoHrrS6SSlww47gVdo5GreBk/YiUqb\nOYlKNMfkVikRD/2dBf98kxJ/Gq+k98SOVmCkdjvgSkQdlbQMohuPAyTS9BMoWI4eOvB6qYpL9dAV\npZHKHZOnwttIlw5VSHobQX4QzEOrC+pVjskn8XKi0qKvEeIUb0Z9wA8KnQn+bHqbYSwhKPr9Q1y/\nai5Pn3crPbwBStI6Z0VRYVh4u20hvnU4eqgKLaiCeWtRo1yUTuXNaDF3SIvVOGxG4rGifJ50A1DY\nsXkkvJVjzWrutCJ8HS/l/XgpAFl2gnyzFqRkmx7EJx0y7ATTh43h2b6nUtIGxtK3Fqsmi0TRIAIF\ny5COTrK4Py3/uV8BFdCVDkCTkhGJCgKOm7NNt5MMSVaxv6iyy0mye10iH3AhklInAcAKK8wx0mYd\ncC+QgmRmshIpJTWahy52ggIrTF87QoadJKy5MxanzH6m+S+yDbNqswkULMdILyPYbzFO0g+O+vDf\nGlRAV9o9RwhKdT8jkpXkWDGGJqso1gP7nZyTq/l4GXdYYhR4HUFOXVGwKjvJMmAY7jT8KwEdd9KR\nJTSW+7LoZUXoaUVY4cvEEnt+fRY8c0uzX2db5c9bV58zF4ZFIH9Vk9wQVQ6fCuhKh1BsBKnWvAwy\nq9niSaHqAJUbLwl253lh0A9BbwSGEeIkjzttyKr7+gNu7ZY7gX7AZjsOUtLbihAXOgmh09OK7PMJ\nYEb/xi9UrSiNpT4XKR1Cup0k005QqvvpbUWo0rzE9lNaIFPz8IvUvuxykviEoIvw1N/0zNG9xIAa\n3JmhJlAGhIRONztOhp1kic8dwXJ0ooKw5qFsr6JiU2Y9zS1r3qXHHe82+/Uqyv6oHrrS7mlSMtCs\nYY03nTXeDLYZIQaaNaRbCQYk3TVAkZJ8s5ZsO44hBHm6j+xvDVkc5UkjE50TcWd/jgGCwktv3U+J\n7md5XZpld/qlTPvup4BnhkxkyKnHtti1K8re1Dh0pUPQpMTZKzhrUgKSYckq4kLHEhoZdvI7ue9v\nsxyHN+Kl7HIS9NL9XODvin4EhbLuP+PWI7kMpRNR49AV5QCcbwVd97FglTeDU+MlAMz3dztoMAcw\nNI0rgo2fXr7lWof8F9UHYKVlqZ84peOSkj5WhJgwiAiDgrpx5C3h+ZueZceTE1vkXC3FrNxGbPMX\nJHauadQnbKX5qICudFhZTpJMO8lSXxZLfVn4pU13O9Zi5+9I+fTopvlUzZtGeGUqNV/NpXbRGwcM\n6lJKEkXLia77lOSutS3c0s5NpVyUDqtC81Lly8SpS7Os8mbiNGA/KSVR6eAX2hHlz/d25dRJ7SKf\nnizbSHTtl0jHxl8wjECvPX+IpG0SWfk+yK+BAiBGYudgApXb8GT12ec4UkpqFr1BcmcYnLNBe4tA\n30JSjhrXshfUSamArnRcQuCwJyB/O8++P2VOkufC2ymTJg6Cy/zdOMmXccj9DmbKrKfbdFA3K7ZS\nPX8a2A8D6YSr7wLHJtDHHVvvmDEQfpAFdXsEEGIgTqL2O8eya3aQ3LkV7PVAEOzfENuYT3DAyWje\nUItdU2elUi6KspcXIkXcIU1qgWVIZsRL2GbHG33cmf5PGt+4IyAt46CPAWJbloJ9L3ADcAnYzxLb\nsLT+dc2XguYLAH/HnXo1Eym/wsjoiZQOTjKClO5nHycZRYiewO6VhbqCyEAmWy7V1ZmpgK50WNWO\nxSYrRq3TsGnojpRscpL8HBC464dOALZajQ/o86ZvavHyANIRRNedgFnVDYBkSW9im4/Zz31hgRuo\nd7P2KZsghEb6KT9ET/k74EN4ryR1xATCKz+g7N2plE9/iPL3ppLctRYjPQ9YD0wDqoGH0TygBTOb\n8UqV3VTKRemQvkxU8Xa8hAIEm5FcHujOsd6DV0TUhCATjS9wGAPEgUXA2CZazHpG/1HQgqkXoUn8\nBSuIbRyJWdobaXoJ9F/ynRI3gb7HkSh6COwQkA76PQQHnLXPNkZKV7LOvtPtkUcqqJj1JNga8G/g\nYqT9OdULz6fLuLtIP/U6ahb+Bid+I3pKb9JHX4fQ9Ba66s5NTSxSOpwqx+Kh2k0sQDIIWA6MQXB/\nWj+Cwg0sSemwzY7jQaOX7quvh/61Geal6A7GAKuBHCPENcEeDV4EoyHO3fAVo2858gqNsa1fEV37\nBdKxCOQfQ3DwGYiDjK+PbxuKWdEDX491eLtt2+82ZuU2ouu+RNoOgYJh+HKHHfB44ZXvE9vYH/gE\n2Fj/vDBOIm30MXi7DjjSS+tUmmNikUq5KB1OuWPSF8GgusdHA90RVDgmAJWOyZ9qN/NJpIjXItt4\nOlKIWZcDPsqTwt2p+XQLdGdiqGeTB3NoXCGvRPEqwivm4sReRiY+ILqhiOi6L/bZRjp72pss6Y0d\nziBQsIxkSZ/69Mu3eTJ7k3rMeUg7Qc3CaZRN/xOJnav3u620bSAPt9LN7oBehpTr0TpxXfi2QAV0\npcPJ1jxsRPJ13eNFwC4kWXX1y9+O7uRmabEch3VI8uw4sxOVe+3v5ThvGgONYJMH892mzHr6iPaL\nF34D9n3AKcDRYD9CdM3JRNZsRkqJWdGd2KaRbvkaR2BH0/HkTKd64f3ENlxO9RdLSFYW1R9PWgkc\n071HUP3la1gVp4PchUy8Ss1Xb5LYtZboxs+Ibf4CJxkFwN9rGOiPAtcAJwHngjiKQN8TOtQizu2R\nyqErHU66ZnBZIIeTY7vogWAHkqsCufXpljInyYV12xrARCSv1i1y0ZJ2PDnxsCszah4PsH2vZ7YD\ni4iuOxs7EkELDCDYb7GbJxcSf68llH/4FDL5AHANMvkWNV/8lIyz7yK6cgaJokUA6NlHYVeuBZYC\nHuB0kCdSs+AV4AoQFUTXPk7mmbfj6dKXtFEXE1k9C2n68WSV4+93Fd6s3k3wriiNoQK60iEd701n\nsBGiQlp0ER5Ce92Uy9X9/NMKMxKIAS8j6LFXGdyW8syQiSw4zHx6cODJJIqeQFpVuEV+/w94E2RX\nkjuXkX7SRjR/tH57O1wKTjqwe4TNNSAfIrw4jFl2PMgZgIFd+iLwALAWGA444CwFHgOuA8Bxbie6\n/nNShp2Lr/sQfN075xqqbZlKuSgdVopm0Fv37xPMAS4K5PCh5iUPQR4Cxwhxmrd1htUdbj5dD2WT\neeYd6OnvA3OBXwLvA8vAeJtEcX/s8J6JUMIbRDolQHndM9VIZwdOci44NwMpgB84EXwmaDeBeBr0\nMaBZwMg9J5fDsCPpR36xSrNTAV3pdFI0g7tS8pmUks8vUwu4NpRXP8qlNRxuPl0PZpF23GUgFgH/\nAK4AapDJD/B0mUWytHf9WHM9kEGg74mgjwLtR6BdiyenH3owATyPuxgfQBFG6un4ejxIoP9SUo/J\nx9d7JIilQCWwFsR2hPe8JrpqpTmoYYuK0kYcbnmAsukPIxMDgDAwGkjFX7AAX94pOIle+HqEEUJg\nhzNIlm/GiS8jtmE00ioFORN4CDfptAPoAsJCT32P9NED0UNdkI5F5WevYVddDPQANqNnvUPmmGsP\nOkxSaRg1bFFROrAt1zakdJjLToSRiRrgPOBxoAp4j/i2JVTPf5/ar8JUz9uGVZtGePVJaP6jkVYS\naa4B+TdgJW4O/g4gFzgJpIldczOVn72EtE0QGnb1GuBW3LIA92NXxImum9XEV640FRXQFaWNeP6m\nZ5l6z6X1j6WU7pdj4ySj9Y9rV06nYvpU3JuXvwJOxJ2xuR5sCfaLwCTMitMIL8/H13MdZmkfzIoA\n8BPcX3sBZALDgO/jLov9EWAgzR9g1RQjLRvkdUCXuhbpwA1Ev5ldP9RRaVvUKBdFaUOcc85Bjr2F\nyOqPiW2cB9ICaQASLZCFr/cw4ltKcfPfj+LmwAUQwQ3UDwA34Y5m6Y0drkAYAn+vtSRLbgMCuHVb\nNGAFcNVeZ88ElgHTEPolCMOL8Nci4/cAv8Ud0f85iG440XK09LwWeU+UhlMBXVHamHEfPswbdANn\nDe4IlMuAcTixdGLrp4LzV+AS4EvALXkLLwIv486LfRLoD5TjxB8hsubneLMHYmT4cGJFSPN6oBhw\ngGNx/yh4gFdB64u3WyZ6ag5CSNJHd6VqztN1x8wF7gc5W80IbaNUykVR2pgVliQZ/wXQC+gK/A63\nbsqd4ITrRrf4gT8AOcDndd9fiJuCyQLOBa4F3sOunInQk4SGvIe/4GPw3Ar0A94EfgDcjfDfhzev\nPykj+pE2+or6GbKezJ6EjjoTdD/C6AP6TwkNOwfNl9qC74jSUKqHrihtTIZw0FiKw7V1z6zADexf\ngXAQxn+QzjogAOJrNN+DOJF1uD35rrh58d3DMDOB9/Hm5VE56ymkPQbs6bhj1/vilhBYRKBgLaFB\nZ++3PcEBp+LtPgA7XIqecjxG6v7rwSitTwV0RWljzvensMJ6hoRcj6WFcJx3QBsFzjgQ+UgnH5w5\n+PuehqY/hhYAO5KJ4/ycxKZluL3vZ4ExwNcY2QOJb5iNTF4P/Ak3kO+pXYMoR+gHDwVGao6q09IO\nqJSLorQxGZqH+1JyuCywkMu9n5I66jL8fUzQhoCzCuwZIKeRKOyOnmriz19JcNBKZKwvsBM31ZIL\nzAPvFFKHj8KsKQV2l7W9BzcH/wSIuxCeD/D3PK51LlZpUk0e0CdPnkzPnj0ZOXIkI0eOZMaMGU19\nCkXp8FI0nVO9GZzmy+ShdQvQAxngnMqeD9WnIM3X8fVcixCgeZJo/v8DxgODgPOBS8AspXLOv7Cr\nBXA3bqrlItBC6GmPEOi7iswz7kDzq5x4R9DkKRchBHfffTd33334s6AURdm/m7eu5O/6crB/AvQC\n8WeMzPx9Vh8y0tJBFIK0cceMvwXyxyBvBzKAV4ArQbPx9xpNytHnqZWEOphmSbm0QjUBRenQBhlB\nHrj6eBADQaSgp7xO2gmX7rONv89ojPRy0E5CGCeC9hMQHtxgDm6aJUb2xKmkjpyognkH1Cw3RR97\n7DFeeOEFjj/+eB5++GEyMjK+s83kyZPrvx87dixjx45tjqYoSoeR+c5aqqZNod9/LDRPYJ/XrJqd\nxLcuwchyCPQdgOZPRdpDqVn0GNjXA9nANLRmWIFJaT6zZ89m9uzZDd7+iIpzjRs3jp07d37n+Qce\neIATTzyRrl27AnDfffdRXFzMc889t+9JVXEuRalX6Zh8nqzBlHCcN0RP7f+3d7cxcdR5HMC/sw8F\n22pBpVvK0qxZwAW67KG09k7bYChV0oDWSxSNHkassY2Xu6TxuBfXBs4ryt2ZJte7xhTwQl+V66WU\nNsoG9QSPxpak+FBLbbgD7hBY7NFiK1j26X8vqivI0y7s7MzOfj+vdobt/H8z2X4Z/jvzmzgAmLMD\n5NG9b+BiR1dg2TM2iLF/1gO+lwDoAP2fkPDAszAmrsPXn7Xim94PIelMgO4qEu4vg2HV2kjsFi1A\njuZcsnZb7O/vR3FxMc6fPx9SUQthoJNWjPo9qL4+gkn8DH7cCR0O4GYHRAn3GW/HU7ckQD9LsE/t\nzPjV2b/BPVwK4JffrtkL3Yq/4pa77kG8+V4I4Ydwj0O/4k5IhmWR2C0KQlR0WxweHg68bmpqgt1u\nD/cQRJrx7uTXuIHd8OMQgN/Cj3r48RP48SXOeaxombw267+b2plReL24eVcpAHQA+Av84y9g/LM1\nuPKPPwMADKvWMsxjQNgDvaKiAjk5OXA4HGhvb8eBAwfCPQSRZnwjJAhMfRZnCoAbABLhxj585pn9\nbKyhvC7wYIy4dTZAVwdgAEA1bjbu2gfgMITnKXzz79Ny7gKpSNi/FD1y5Ei4N0mkKW7hx4jfjRWS\nHhuMBnzsqYIbObh5NcrPcbOdLSDhHBIWOOV6L/5dFKRuBbxnMP75SxCTTwDY+P0bRBr8kx/JtCek\nNrz1nyiChn2TODD+P3jFHfDiS9y/bCWeiDfgrckd8MCPSeGBQBsknIEB7+Gx+Pn7pnS09OLsv3bi\nvp2AMSkB1zrvhO/6GUD8CMBlQP97xK3dFpmdI8Ux0IkiqHbiGr4WfwDwIoAxfOjORdZyD353280r\nw8b9Ppz3fgo/gPWGNbhNt/B/UWfaBvg2/Ao3+nNw672fYOLSObhd/wUM9ViZ+WPEJWfLuk+kHgx0\nogi67L8O4KlvlxLgQzGG/Ufx3aUDK3R6bFq2KuTt7jnbgD9ufBaGW7/Cqo1p8IwlQL/8BeiW8clC\nsYTNuYgi6A7dSgB//3bpGvR4C2t0S7/6JFE3gvakY4FlY8JlhnkMYqATRdDO5auwQvoF4pEBI9Zh\ng3EcdsOKsGy7o6UXQ4eKw7Itik6cciGKoBR9HF65NRku3ySWS7djtT6814bXZhZj6OIprN19Kqzb\npejAM3SiCBrxWaBHHCyGW5CkW4ZBXzrCfa92bSbP0mMVA50ogv7js6Pd/TQ8wohPvFvxqacAXoT/\nDs7vbjqi2MJAJ4qgDcZTWCldxbEbv8GALwsPxh2BUXLLMhbn02MPA50owuKkcQCADj7o4ZFtnNrM\nYp6pxxgGOlEEfeotgMtnxWPxNbhDN4h299PwCqOsY05t5EXaxkAniqBEnQsPxh1BvDSB+4wncJf+\nY+jglXXMhvI6vPLrny78Rop6DHSiCFqnv4B4aQIAIEmA1fARdJL8j2z0P/SQ7GOQ8hjoRDHivfh3\nlS6BZMZAJ4oRHS29nE/XOAY6UQxpKK/D2dqdSpdBMmGgE8UYZ9oGpUsgmTDQiWIQr0/XJgY6UYxi\nqGsPA50ohrE9gLYw0IliGDszagsDnSjGVb1/mJczagQDnYjQUF6HzAfuUboMWiIGOhEBAEpfeVHp\nEmiJGOhEFMCpl+jGQCeigIbyOl75EsUY6EQ0TW1mMdvtRikGOhHNwHa70YmBTkSz4p2k0YeBTkRz\nYg/16MJAJ6I5dbT0st1uFGGgE9G82G43ejDQiWhBVe8f5uWMUYCBTkRBqc0sZnsAlWOgE1HQ2B5A\n3RjoRBQSTr2oFwOdiEJSm1nMUFcpBjoRhYzz6erEQCeiReF8uvow0Ilo0Xgnqbow0Ilo0Tpaehnq\nKsJAJ6Il6WjpZbtdlWCgE9GSsd2uOiw60I8dO4bs7Gzo9Xp0dXVN+9mrr76K9PR02Gw2tLa2LrlI\nIlI/Tr0ob9GBbrfb0dTUhC1btkxb393djcbGRnR3d8PpdGL37t3w+/mcQiKt62jpZQ91hS060G02\nGzIyMmasb25uxpNPPgmj0QiLxYK0tDR0dnYuqUgiih4MdeUYwr3BoaEhbNq0KbBsNpsxODg4432V\nlZWB1/n5+cjPzw96jF1jny+lRCKS2S6lC9CItrY2tLW1Bf3+eQO9sLAQLpdrxvrq6moUFwd/668k\nSTPWTQ10IiKa6Ycnu1VVVfO+f95Af+edd0IuICUlBQMDA4HlL774AikpKSFvh4iIQhOWyxaFEIHX\nJSUlOHr0KNxuN/r6+tDT04ONGzeGYxgiIprHogO9qakJqampOHPmDLZv346ioiIAQFZWFh5//HFk\nZWWhqKgIhw4dmnXKhYiIwksSU0+vIzWoJGGpw7a1tYX0RWqs4nEKDo9TcHicgifHsVooO6P2TtFQ\nviVc/fUAAAM+SURBVPmNZTxOweFxCg6PU/CUOFZRG+hERDQdA52ISCMUm0MnIqLQzRfZYb9TNBgK\n/A4hItI8TrkQEWkEA52ISCMY6EREGhF1gc4Ha4SusrISZrMZubm5yM3NhdPpVLokVXE6nbDZbEhP\nT0dNTY3S5aiaxWJBTk4OcnNz2dJjiueeew4mkwl2uz2w7sqVKygsLERGRga2bduGsbEx+QsRUebi\nxYvi0qVLIj8/X5w7dy6w/sKFC8LhcAi32y36+vqE1WoVPp9PwUrVo7KyUrz++utKl6FKXq9XWK1W\n0dfXJ9xut3A4HKK7u1vpslTLYrGI0dFRpctQnQ8++EB0dXWJ9evXB9a9/PLLoqamRgghxGuvvSYq\nKipkryPqztD5YI3FEbyyaFadnZ1IS0uDxWKB0WhEaWkpmpublS5L1fhZmmnz5s1ITEyctu7kyZMo\nKysDAJSVleHEiROy1xF1gT6XoaEhmM3mwPJcD9aIVQcPHoTD4UB5eXlk/vSLEoODg0hNTQ0s83Mz\nP0mSsHXrVuTl5aG2tlbpclRtZGQEJpMJAGAymTAyMiL7mIpch74QOR+soVVzHbP9+/dj165d2Ldv\nHwBg79692LNnD+rr6yNdoirF0mckHE6fPo3k5GRcvnwZhYWFsNls2Lx5s9JlqZ4kSRH5rKky0Plg\njdAFe8yef/75kH4pat0PPzcDAwPT/tKj6ZKTkwEASUlJ2LFjBzo7OxnoczCZTHC5XFizZg2Gh4ex\nevVq2ceM6ikXwQdrBGV4eDjwuqmpado38bEuLy8PPT096O/vh9vtRmNjI0pKSpQuS5UmJiZw/fp1\nAMD4+DhaW1v5WZpHSUkJGhoaAAANDQ149NFH5R9U9q9dw+z48ePCbDaL+Ph4YTKZxMMPPxz42f79\n+4XVahV33323cDqdClapLs8884yw2+0iJydHPPLII8Llcildkqq8/fbbIiMjQ1itVlFdXa10OarV\n29srHA6HcDgcIjs7m8dqitLSUpGcnCyMRqMwm83izTffFKOjo6KgoECkp6eLwsJCcfXqVdnrUKQ5\nFxERhV9UT7kQEdH3GOhERBrBQCci0ggGOhGRRjDQiYg0goFORKQR/wdL2U2tfpkOJQAAAABJRU5E\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1128a3ed0>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-Means Clustering"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class KMeans():\n",
" def __init__(self, k):\n",
" self.k = k\n",
" \n",
" def fit(self, X, y=None):\n",
" k = self.k\n",
" # Assign points to K random groups of roughly equal sizes\n",
" group = zeros(len(X))\n",
" new_group = permutation(concatenate([i*ones(l) for i, l in enumerate([len(l) for l in array_split(X, k)])]))\n",
" \n",
" # Do until no change\n",
" while not array_equal(new_group, group):\n",
" group = new_group\n",
" # Calculate the new cluster means\n",
" means = array([mean(X[group==i], axis=0) for i in range(k)])\n",
" # Re-assign each point to the closest cluster\n",
" new_group = argmax(vstack([-sum((X-m)**2, axis=1) for m in means]).T, axis=1) \n",
" \n",
" self.means = means\n",
" return self\n",
" \n",
" def predict(self, X):\n",
" # For each point, return the index of the closest cluster\n",
" return argmax(vstack([-sum((X-m)**2, axis=1) for m in self.means]).T, axis=1)\n",
"\n",
"km = KMeans(4)\n",
"test(km)\n",
"scatter(km.means[:,0],km.means[:,1], marker='x', s=200, c='w')\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Accuracy: 0.26\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.collections.PathCollection at 0x111ff4110>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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unV3QPTG4s3tirpeKbNeBuUBLZPk7Zs3ykZwMQ4a8gNcUy+RZv7Bk414+mzKC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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x111ff4c90>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SVM"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def linear_kernel(x1, x2):\n",
" return np.dot(x1, x2)\n",
" \n",
"def polynomial_kernel(x, y, p=3):\n",
" return (1 + np.dot(x, y)) ** p\n",
" \n",
"def gaussian_kernel(x, y, sigma=5.0):\n",
" return np.exp(-linalg.norm(x-y)**2.0 / (2.0 * (sigma ** 2.0)))\n",
"\n",
"class SVM():\n",
" def __init__(self, C=None, kernel=gaussian_kernel):\n",
" self.kernel = kernel\n",
" self.C = C\n",
" \n",
" def fit(self, X, y):\n",
" n_samples, n_features = X.shape\n",
" y = array(y, dtype=float)\n",
" # Because this is a minimisation problem instead\n",
" # of a maximisation problem, components of the\n",
" # lagrangian are multiplied by -1\n",
"\n",
" \n",
" # Build P, an NxN matrix of\n",
" # y_n * y_m * k(x_n, x_m) for all combinations of x\n",
" # where k is the kernel function (dot product)\n",
" K = np.zeros((n_samples, n_samples))\n",
" for i in range(n_samples):\n",
" for j in range(n_samples):\n",
" # Dot product of training samples\n",
" K[i,j] = self.kernel(X[i], X[j])\n",
" P = cvxopt.matrix(outer(y,y) * K)\n",
" \n",
" # coefficient of a_n is -1\n",
" q = cvxopt.matrix(ones(n_samples) * -1)\n",
" \n",
" # Equality constraint: A*a = 0\n",
" A = cvxopt.matrix(y, (1,n_samples))\n",
" b = cvxopt.matrix(0.0)\n",
" \n",
" # Inequality constraint\n",
" if self.C is None:\n",
" # Hard margin, G*a > h\n",
" # 1*a > 0\n",
" G = cvxopt.matrix(diag(np.ones(n_samples) * -1))\n",
" h = cvxopt.matrix(zeros(n_samples))\n",
" else:\n",
" # Soft margin, C > a > 0\n",
" tmp1 = np.diag(np.ones(n_samples) * -1)\n",
" tmp2 = np.identity(n_samples)\n",
" G = cvxopt.matrix(np.vstack((tmp1, tmp2)))\n",
" tmp1 = np.zeros(n_samples)\n",
" tmp2 = np.ones(n_samples) * self.C\n",
" h = cvxopt.matrix(np.hstack((tmp1, tmp2)))\n",
" \n",
" \n",
" # Solve QP problem\n",
" solution = solvers.qp(P, q, G, h, A, b)\n",
" \n",
" # Solution gives the lagrange multipliers\n",
" a = np.ravel(solution['x'])\n",
" \n",
" # The only interesting ones are > 0\n",
" sv = a>1e-5\n",
" ind = np.arange(len(a))[sv]\n",
" \n",
" # Get X and y of interesting support vectors\n",
" self.sv_x = X[sv]\n",
" self.sv_y = y[sv]\n",
" self.a = a[sv]\n",
" \n",
" # Calculate intercept:\n",
" # sum of:\n",
" # target - sum of: support vectors * target * k(x, m)\n",
" self.b = 0\n",
" for n in range(len(self.a)):\n",
" self.b += self.sv_y[n]\n",
" self.b -= sum(self.a * self.sv_y * K[ind[n],sv])\n",
" self.b /= len(self.a)\n",
" \n",
" return self\n",
" \n",
" def predict(self, X):\n",
" y_predict = np.zeros(len(X))\n",
" # Loop through each observation i\n",
" for i in range(len(X)):\n",
" # Decision variable s=y(x)\n",
" s = 0\n",
" # Loop through each support vec n\n",
" for a, sv_y, sv in zip(self.a, self.sv_y, self.sv_x):\n",
" # Add the vectorweight * classn * kernel(x_i, vec_n)\n",
" s += a * sv_y * self.kernel(X[i], sv)\n",
" \n",
" y_predict[i] = s\n",
" \n",
" # Classification determined by sign of decision variable\n",
" return sign(y_predict + self.b)\n",
"\n",
"svm = SVM(C=10000, kernel=polynomial_kernel)\n",
"test(svm)\n",
"\n",
"scatter(svm.sv_x[:,0], svm.sv_x[:,1], \n",
" edgecolors='w', facecolors='none', s=svm.a)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" pcost dcost gap pres dres\n",
" 0: 2.8055e+06 -1.1659e+10 3e+10 9e-01 3e-07\n",
" 1: 8.4949e+06 -4.1549e+09 7e+09 1e-01 2e-07\n",
" 2: 1.1610e+07 -9.1405e+08 2e+09 3e-02 8e-08\n",
" 3: 1.0783e+07 -9.1895e+07 2e+08 3e-03 2e-08\n",
" 4: 4.7339e+06 -2.3951e+07 4e+07 5e-04 4e-09\n",
" 5: 1.2532e+06 -4.2385e+06 6e+06 5e-05 4e-10\n",
" 6: 2.3513e+05 -3.7593e+05 6e+05 2e-06 7e-11\n",
" 7: 3.4798e+04 -3.7200e+04 7e+04 3e-15 3e-11\n",
" 8: 4.9779e+03 -5.5267e+03 1e+04 2e-15 8e-12\n",
" 9: 7.0883e+02 -7.9389e+02 2e+03 9e-16 4e-12\n",
"10: 9.9858e+01 -1.1550e+02 2e+02 3e-16 2e-12\n",
"11: 1.3632e+01 -1.7214e+01 3e+01 2e-16 4e-13\n",
"12: 1.6675e+00 -2.7375e+00 4e+00 2e-16 2e-13\n",
"13: 9.6444e-02 -5.1820e-01 6e-01 2e-16 8e-14\n",
"14: -6.3099e-02 -2.3787e-01 2e-01 2e-16 4e-14\n",
"15: -1.0860e-01 -2.1816e-01 1e-01 2e-16 2e-14\n",
"16: -1.3660e-01 -1.9241e-01 6e-02 2e-16 2e-14\n",
"17: -1.5111e-01 -1.7343e-01 2e-02 2e-16 2e-14\n",
"18: -1.5756e-01 -1.6728e-01 1e-02 2e-16 3e-14\n",
"19: -1.6149e-01 -1.6240e-01 9e-04 2e-16 3e-14\n",
"20: -1.6189e-01 -1.6190e-01 1e-05 2e-16 2e-14\n",
"21: -1.6190e-01 -1.6190e-01 1e-07 2e-16 3e-14\n",
"Optimal solution found.\n",
"Accuracy: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1.0\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1694,
"text": [
"<matplotlib.collections.PathCollection at 0x112bb4f90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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FrQXosif79jzArmf/pd3n9Mlui0rPIzSdQOnowx/YTdrby9wVGmvN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\nOTV/qQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x112426e10>"
]
}
],
"prompt_number": 1694
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1699,
"text": [
"array([ 90.5050762 , 89.97171731, 93.92318467, 119.5379999 ,\n",
" 103.85869242, 103.76915744, 113.1565638 , 89.73534053,\n",
" 103.81797994, 91.7242878 ])"
]
}
],
"prompt_number": 1699
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"test(svm)\n",
"scatter(svm.sv_x[:,0], svm.sv_x[:,1], \n",
" edgecolors='w', facecolors='none', s=(scale(svm.a)-min(scale(svm.a)))*50)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" pcost dcost gap pres dres\n",
" 0: 2.8055e+06 -1.1659e+10 3e+10 9e-01 3e-07\n",
" 1: 8.4949e+06 -4.1549e+09 7e+09 1e-01 2e-07\n",
" 2: 1.1610e+07 -9.1405e+08 2e+09 3e-02 8e-08\n",
" 3: 1.0783e+07 -9.1895e+07 2e+08 3e-03 2e-08\n",
" 4: 4.7339e+06 -2.3951e+07 4e+07 5e-04 4e-09\n",
" 5: 1.2532e+06 -4.2385e+06 6e+06 5e-05 4e-10\n",
" 6: 2.3513e+05 -3.7593e+05 6e+05 2e-06 7e-11\n",
" 7: 3.4798e+04 -3.7200e+04 7e+04 3e-15 3e-11\n",
" 8: 4.9779e+03 -5.5267e+03 1e+04 2e-15 8e-12\n",
" 9: 7.0883e+02 -7.9389e+02 2e+03 9e-16 4e-12\n",
"10: 9.9858e+01 -1.1550e+02 2e+02 3e-16 2e-12\n",
"11: 1.3632e+01 -1.7214e+01 3e+01 2e-16 4e-13\n",
"12: 1.6675e+00 -2.7375e+00 4e+00 2e-16 2e-13\n",
"13: 9.6444e-02 -5.1820e-01 6e-01 2e-16 8e-14\n",
"14: -6.3099e-02 -2.3787e-01 2e-01 2e-16 4e-14\n",
"15: -1.0860e-01 -2.1816e-01 1e-01 2e-16 2e-14\n",
"16: -1.3660e-01 -1.9241e-01 6e-02 2e-16 2e-14\n",
"17: -1.5111e-01 -1.7343e-01 2e-02 2e-16 2e-14\n",
"18: -1.5756e-01 -1.6728e-01 1e-02 2e-16 3e-14\n",
"19: -1.6149e-01 -1.6240e-01 9e-04 2e-16 3e-14\n",
"20: -1.6189e-01 -1.6190e-01 1e-05 2e-16 2e-14\n",
"21: -1.6190e-01 -1.6190e-01 1e-07 2e-16 3e-14\n",
"Optimal solution found.\n",
"Accuracy: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1.0\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1707,
"text": [
"<matplotlib.collections.PathCollection at 0x119862050>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ttP3GqpUmBnMpBZENh+PM2oIzq4h42QB7LZWB3+wyuOhI6Ym9OG8Z0BN4FRiA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\nU1I6jAOVZmkKt9A41BngUGcAr2i7Urn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sUU3m74YN5XWiR7QG/Nwi8IJmHLoIPaufmIdv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\nXQghQoQEuhBChIj/B7qbLBA1YMY9AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1191e1f50>"
]
}
],
"prompt_number": 1707
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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